For all these to happen, the systems are not necessary to be coded or programmed to execute the above tasks. The Artificial Intelligence/ Machine learning is all about focusing on the existing algorithms and based on the observation and data exposure modifying the algorithm to define or to conclude an action. This is the primary aspect of machine learning utilization.

In a sense, we can consider Machine Learning as Data Mining but it is not exactly the same. In both the scenarios the data is sorted and searched and looks for a certain pattern. In Data Mining, the data is extracted for a certain use but in case of Machine Learning, it uses the data and at the same time analyzes and modifies the existing algorithms, so it is more sort of an update process.

Fascinated about Machine Learning and the wonders that one can create with the perfect utilization of this?

So, let’s not wait further, the required skills for an individual to master or to crack a Machine Learning job they need to possess the following skills:

Programming Knowledge in Python/C++/ Java:

If you are looking for a Machine Learning job then it is mandatory to have knowledge on one of this programming knowledge. It is advised to learn these languages and based on the need one can adjust and work towards the goal. In general C++ language is helpful because it will help the individual to write the code fast compared to the rest.

In general, if the individual is very good in Java then they have to apply mappers and reducers as this is required to utilize Java.

Probability and Statistics knowledge:

These theories will definitely help the individual to increase his capacity in terms of understanding and building algorithms. Some of the best theories that they can go through straight away is Naive Bayes, Gaussian Mixture models, and Hidden Markov models.

Understanding this model will help the individual to brush up his knowledge on probability and statistics and also help them to build better algorithms. One has to go nuts and has to spend a good amount of time in terms of studying all these different aspects of Statistics.

Applied Math and Algorithms knowledge:

One should have a good understanding of how to build an algorithm from scratch and also need to understand the algorithm theory. The next step is to understand how the algorithm works. Further to master this one has to go through the following subjects:

Distributed computing is essential because the modern data is huge and only one machine cannot help and compile the data for analysis purposes. So the need of distributed computing has risen to next level and the service providers like Amazon Ec2, Apache Hadoop made it possible. With the help of this distribute environments the data is distributed across the cluster and thus the data processed is simplified and at the same time, the data analysis is quicker compared to single computing way.

Expertise in Unix Tools and further expansion:

One should have a good working knowledge of all available Unix tools which will help them to compile their tasks easier. The exposure towards cat, grep, find, awk, sed, sort, cut is definitely needed. Also, most of the time the processing happens only on the Linux based machines so it is vital for individuals to have exposure towards Unix tools as well. Utilizing these tools have definitely made the life a lot easier.

In Depth knowledge on Advanced Signal Processing techniques:

The need of in depth knowledge on Advanced Signal Processing is to make sure that the individual can cater to all types of problems and this comes only with experience and expectations. It is a known fact that every problem is handled in a unique way so it is very important for the individual to know as much as possible in term of Signal processing. It is always good for individuals to go over time-frequency analysis, Fourier analysis, and convolution etc.

Update yourself:

To sustain in this industry one has to make sure that they are up to date with all the information and industry latest trends and happenings. This will help them to understand the advancements and at the same time help them to go over the challenges that others have faced in Machine Learning implementations. The individual should be more consultative in approach when it comes to any update rather than being a passive reader.

Read a lot on Machine Learning, utilize the resources:

There are a lot of resources available on the internet about machine learning. So the only catch is the individual's time and commitment towards reading these available materials. There are numerous amount of books available over the internet and in libraries that cater towards Machine learning basics and it’s utilization so one has to make sure and benefit out of it.